Client Testimonials

I liked the logic exercises (writing rules conditions) on the 2nd day.

Jan Janke- CERN

Applied Machine Learning

ref material to use later was very good.

PAUL BEALES- Seagate Technology.

Introduction to Drools 6 for Developers

Lots of exercises, which were good and which were well-administered.

Joseph Richardson - Sandia National Labs

Predictive Modelling with R

He was very informative and helpful.

Pratheep Ravy - UPC Schweiz GmbH

Artificial Neural Networks, Machine Learning, Deep Thinking

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
Bias-Variance trade-off
Machine Learning with Python
Choice of libraries
Add-on tools
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
K-Nearest neighbors
Exercises
Cross-validation and Resampling
Cross-validation approaches
Bootstrap
Exercises
Unsupervised Learning
K-means clustering
Examples
Challenges of unsupervised learning and beyond K-means

The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
Bias-Variance trade-off
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
K-Nearest neighbors
Exercises
Cross-validation and Resampling
Cross-validation approaches
Bootstrap
Exercises
Unsupervised Learning
K-means clustering
Examples
Challenges of unsupervised learning and beyond K-means

TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.
Audience
This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects
After completing this course, delegates will:
understand TensorFlow’s structure and deployment mechanisms
be able to carry out installation / production environment / architecture tasks and configuration
be able to assess code quality, perform debugging, monitoring
be able to implement advanced production like training models, building graphs and logging
Machine Learning and Recursive Neural Networks (RNN) basics
NN and RNN
Backprogation
Long short-term memory (LSTM)
TensorFlow Basics
Creation, Initializing, Saving, and Restoring TensorFlow variables
Feeding, Reading and Preloading TensorFlow Data
How to use TensorFlow infrastructure to train models at scale
Visualizing and Evaluating models with TensorBoard
TensorFlow Mechanics 101
Prepare the Data
Download
Inputs and Placeholders
Build the Graph
Inference
Loss
Training
Train the Model
The Graph
The Session
Train Loop
Evaluate the Model
Build the Eval Graph
Eval Output
Advanced Usage
Threading and Queues
Distributed TensorFlow
Writing Documentation and Sharing your Model
Customizing Data Readers
Using GPUs
Manipulating TensorFlow Model Files
TensorFlow Serving
Introduction
Basic Serving Tutorial
Advanced Serving Tutorial
Serving Inception Model Tutorial

Audience:
This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
Big Data Overview:
What is Big Data
Why Big Data is gaining popularity
Big Data Case Studies
Big Data Characteristics
Solutions to work on Big Data.
Hadoop & Its components:
What is Hadoop and what are its components.
Hadoop Architecture and its characteristics of Data it can handle /Process.
Brief on Hadoop History, companies using it and why they have started using it.
Hadoop Frame work & its components- explained in detail.
What is HDFS and Reads -Writes to Hadoop Distributed File System.
How to Setup Hadoop Cluster in different modes- Stand- alone/Pseudo/Multi Node cluster.
(This includes setting up a Hadoop cluster in VM BOX/VMware, Network configurations that need to be carefully looked into, running Hadoop Daemons and testing the cluster).
What is Map Reduce frame work and how it works.
Running Map Reduce jobs on Hadoop cluster.
Understanding Replication , Mirroring and Rack awareness in context of Hadoop clusters.
Hadoop Cluster Planning:
How to plan your hadoop cluster.
Understanding hardware-software to plan your hadoop cluster.
Understanding workloads and planning cluster to avoid failures and perform optimum.
What is MapR and why MapR :
Overview of MapR and its architecture.
Understanding & working of MapR Control System, MapR Volumes , snapshots & Mirrors.
Planning a cluster in context of MapR.
Comparison of MapR with other distributions and Apache Hadoop.
MapR installation and cluster deployment.
Cluster Setup & Administration:
Managing services, nodes ,snapshots, mirror volumes and remote clusters.
Understanding and managing Nodes.
Understanding of Hadoop components, Installing Hadoop components alongside MapR Services.
Accessing Data on cluster including via NFS Managing services & nodes.
Managing data by using volumes, managing users and groups, managing & assigning roles to nodes, commissioning decommissioning of nodes, cluster administration and performance monitoring, configuring/ analyzing and monitoring metrics to monitor performance, configuring and administering MapR security.
Understanding and working with M7- Native storage for MapR tables.
Cluster configuration and tuning for optimum performance.
Cluster upgrade and integration with other setups:
Upgrading software version of MapR and types of upgrade.
Configuring Mapr cluster to access HDFS cluster.
Setting up MapR cluster on Amazon Elastic Mapreduce.
All the above topics include Demonstrations and practice sessions for learners to have hands on experience of the technology.

Goal:
Learning to work with SPSS at the level of independence
The addressees:
Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining techniques.
Using the program
The dialog boxes
input / downloading data
the concept of variable and measuring scales
preparing a database
Generate tables and graphs
formatting of the report
Command language syntax
automated analysis
storage and modification procedures
create their own analytical procedures
Data Analysis
descriptive statistics
Key terms: eg variable, hypothesis, statistical significance
measures of central tendency
measures of dispersion
measures of central tendency
standardization
Introduction to research the relationships between variables
correlational and experimental methods
Summary: This case study and discussion

This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
Current state of the technology
What is used
What may be potentially used
Rules based AI
Simplifying decision
Machine Learning
Classification
Clustering
Neural Networks
Types of Neural Networks
Presentation of working examples and discussion
Deep Learning
Basic vocabulary
When to use Deep Learning, when not to
Estimating computational resources and cost
Very short theoretical background to Deep Neural Networks
Deep Learning in practice (mainly using TensorFlow)
Preparing Data
Choosing loss function
Choosing appropriate type on neural network
Accuracy vs speed and resources
Training neural network
Measuring efficiency and error
Sample usage
Anomaly detection
Image recognition
ADAS

Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.
Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov.
Audience
This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.
Getting Started
DL4J Examples in a Few Easy Steps
Using DL4J In Your Own Projects: Configuring the POM.xml File
Word2Vec
Introduction
Neural Word Embeddings
Amusing Word2vec Results
the Code
Anatomy of Word2Vec
Setup, Load and Train
A Code Example
Troubleshooting & Tuning Word2Vec
Word2vec Use Cases
Foreign Languages
GloVe (Global Vectors) & Doc2Vec

The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
Bias-Variance trade-off
Machine Learning with Python
Choice of libraries
Add-on tools
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
K-Nearest neighbors
Exercises
Cross-validation and Resampling
Cross-validation approaches
Bootstrap
Exercises
Unsupervised Learning
K-means clustering
Examples
Challenges of unsupervised learning and beyond K-means

The training is aimed at people who want to learn the basics of neural networks and their applications.
The Basics
Whether computers can think of?
Imperative and declarative approach to solving problems
Purpose Bedan on artificial intelligence
The definition of artificial intelligence. Turing test. Other determinants
The development of the concept of intelligent systems
Most important achievements and directions of development
Neural Networks
The Basics
Concept of neurons and neural networks
A simplified model of the brain
Opportunities neuron
XOR problem and the nature of the distribution of values
The polymorphic nature of the sigmoidal
Other functions activated
Construction of neural networks
Concept of neurons connect
Neural network as nodes
Building a network
Neurons
Layers
Scales
Input and output data
Range 0 to 1
Normalization
Learning Neural Networks
Backward Propagation
Steps propagation
Network training algorithms
range of application
Estimation
Problems with the possibility of approximation by
Examples
XOR problem
Lotto?
Equities
OCR and image pattern recognition
Other applications
Implementing a neural network modeling job predicting stock prices of listed
Problems for today
Combinatorial explosion and gaming issues
Turing test again
Over-confidence in the capabilities of computers

This three-day course provides a comprehensive introduction to the MATLAB technical computing environment. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:
Working with the MATLAB user interface
Entering commands and creating variables
Analyzing vectors and matrices
Visualizing vector and matrix data
Working with data files
Working with data types
Automating commands with scripts
Writing programs with logic and flow control
Writing functions
Part 1
A Brief Introduction to MATLAB
Objectives: Offer an overview of what MATLAB is, what it consists of, and what it can do for you
An Example: C vs. MATLAB
MATLAB Product Overview
MATLAB Application Fields
What MATLAB can do for you?
The Course Outline
Working with the MATLAB User Interface
Objective: Get an introduction to the main features of the MATLAB integrated design environment and its user interfaces. Get an overview of course themes.
MATALB Interface
Reading data from file
Saving and loading variables
Plotting data
Customizing plots
Calculating statistics and best-fit line
Exporting graphics for use in other applications
Va​riables and Expressions
Objective: Enter MATLAB commands, with an emphasis on creating and accessing data in variables.
Entering commands
Creating variables
Getting help
Accessing and modifying values in variables
Creating character variables
Analysis and Visualization with Vectors
Objective: Perform mathematical and statistical calculations with vectors, and create basic visualizations. See how MATLAB syntax enables calculations on whole data sets with a single command.
Calculations with vectors
Plotting vectors
Basic plot options
Annotating plots
Analysis and Visualization with Matrices
Objective: Use matrices as mathematical objects or as collections of (vector) data. Understand the appropriate use of MATLAB syntax to distinguish between these applications.
Size and dimensionality
Calculations with matrices
Statistics with matrix data
Plotting multiple columns
Reshaping and linear indexing
Multidimensional arrays
Part 2
Automating Commands with Scripts
Objective: Collect MATLAB commands into scripts for ease of reproduction and experimentation. As the complexity of your tasks increases, entering long sequences of commands in the Command Window becomes impractical.
A Modelling Example
The Command History
Creating script files
Running scripts
Comments and Code Cells
Publishing scripts
Working with Data Files
Objective: Bring data into MATLAB from formatted files. Because imported data can be of a wide variety of types and formats, emphasis is given to working with cell arrays and date formats.
Importing data
Mixed data types
Cell arrays
Conversions amongst numerals, strings, and cells
Exporting data
Multiple Vector Plots
Objective: Make more complex vector plots, such as multiple plots, and use color and string manipulation techniques to produce eye-catching visual representations of data.
Graphics structure
Multiple figures, axes, and plots
Plotting equations
Using color
Customizing plots
Logic and Flow Control
Objective: Use logical operations, variables, and indexing techniques to create flexible code that can make decisions and adapt to different situations. Explore other programming constructs for repeating sections of code, and constructs that allow interaction with the user.
Logical operations and variables
Logical indexing
Programming constructs
Flow control
Loops
Matrix and Image Visualization
Objective: Visualize images and matrix data in two or three dimensions. Explore the difference in displaying images and visualizing matrix data using images.
Scattered Interpolation using vector and matrix data
3-D matrix visualization
2-D matrix visualization
Indexed images and colormaps
True color images
Part 3
Data Analysis
Objective: Perform typical data analysis tasks in MATLAB, including developing and fitting theoretical models to real-life data. This leads naturally to one of the most powerful features of MATLAB: solving linear systems of equations with a single command.
Dealing with missing data
Correlation
Smoothing
Spectral analysis and FFTs
Solving linear systems of equations
Writing Functions
Objective: Increase automation by encapsulating modular tasks as user-defined functions. Understand how MATLAB resolves references to files and variables.
Why functions?
Creating functions
Adding comments
Calling subfunctions
Workspaces
Subfunctions
Path and precedence
Data Types
Objective: Explore data types, focusing on the syntax for creating variables and accessing array elements, and discuss methods for converting among data types. Data types differ in the kind of data they may contain and the way the data is organized.
MATLAB data types
Integers
Structures
Converting types
File I/O
Objective: Explore the low-level data import and export functions in MATLAB that allow precise control over text and binary file I/O. These functions include textscan, which provides precise control of reading text files.
Opening and closing files
Reading and writing text files
Reading and writing binary files
Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.
Conclusion
Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.
Objectives: Summarise what we have learnt
A summary of the course
Other upcoming courses on MATLAB
Note that the course might be subject to few minor discrepancies when being delivered without prior notifications.

Limiting results
The WHERE clause
Comparison operators
LIKE Condition
Prerequisite BETWEEN ... AND
IS NULL condition
Condition IN
Boolean operators AND, OR and NOT
Many of the conditions in the WHERE clause
The order of the operators.
DISTINCT clause
SQL functions
The differences between the functions of one and multilines
Features text, numeric, date,
Explicit and implicit conversion
Conversion functions
Nesting functions
Viewing the performance of the functions - dual table
Getting the current date function SYSDATE
Handling of NULL values
Aggregating data using the grouping function
Grouping functions
How grouping functions treat NULL values
Create groups of data - the GROUP BY clause
Grouping multiple columns
Limiting the function result grouping - the HAVING clause
Subqueries
Place subqueries in the SELECT command
Subqueries single and multi-lineage
Operators Subqueries single-line
Features grouping in subquery
Operators Subqueries multi-IN, ALL, ANY
How NULL values ​​are treated in subqueries
Operators collective
UNION operator
UNION ALL operator
INTERSECT operator
MINUS operator
Further Usage Of Joins
Revisit Joins
Combining Inner and Outer Joins
Partitioned Outer Joins
Hierarchical Queries
Further Usage Of Sub-Queries
Revisit sub-queries
Use of sub-queries as virtual tables/inline views and columns
Use of the WITH construction
Combining sub-queries and joins
Analytics functions
OVER clause
Partition Clause
Windowing Clause
Rank, Lead, Lag, First, Last functions
Retrieving data from multiple tables (if time at end)
Types of connectors
The use NATURAL JOIN
Aliases tables
Joins in the WHERE clause
INNER JOIN Inner join
External Merge LEFT, RIGHT, FULL OUTER JOIN
Cartesian product
Aggregate Functions (if time at end)
Revisit Group By function and Having clause
Group and Rollup
Group and Cube

Coding interfaces which allow users to get what they want easily is hard. This course guides you how to create effective UI with newest technologies and libraries.
It introduces idea of coding logic in Rule Engines (mostly Nools and PHP Rules) to make it easier to modify and test. After that the courses shows a way of integrating the logic on the front end of the website using JavaScript. Logic coded this way can be reused on the backend.
Writing your rules
Available rule engines
Stating rules in a declarative manner
Extending rules
Create unit tests for the rules
Available test frameworks
Running tests automatically
Creating GUI for the rules
Available frameworks
GUI design principles
Integrating logic with the GUI
Running rules in the browser
Ajax
Decision tables
Create functional tests for the GUI
Available frameworks
Testing against multiple browsers

The Semantic Web is a collaborative movement led by the World Wide Web Consortium (W3C) that promotes common formats for data on the World Wide Web. The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries.
Semantic Web Overview
Introduction
Purpose
Standards
Ontology
Projects
Resource Description Framework (RDF)
Introduction
Motivation and Goals
RDF Concepts
RDF Vocabulary URI and Namespace (Normative)
Datatypes (Normative)
Abstract Syntax (Normative)
Fragment Identifiers

This course is intended for data engineers, decision makers and data analysts and will lead you to create very effective plots using R studio that appeal to decision makers and help them find out hidden information and take the right decisions
Day 1:
overview of R programming
introduction to data visualization
scatter plots and clusters
the use of noise and jitters
Day 2:
other type of 2D and 3D plots
histograms
heat charts
categorical data plotting
Day 3:
plotting KPIs with data
R and X charts examples
dashboards
parallel axes
mixing categorical data with numeric data
Day 4:
different hats of data visualization
disguised and hidden trends
case studies
saving plots and loading Excel files

This course explain how to write declarative rules using PHP Business Rules (http://sourceforge.net/projects/phprules/). It shows how to write, organize and integrate rules with existing code. Most of the course is based on exercises preceded with short introduction and examples.
Short Introduction to Rule Engines
Artificial Intelligence
Expert Systems
What is a Rule Engine?
Why use a Rule Engine?
Advantages of a Rule Engine
When should you use a Rule Engine?
Scripting or Process Engines
When you should NOT use a Rule Engine
Strong and Loose Coupling
What are rules?
Creating and Implementing Rules
Fact Model
Rule independence
Priority, flags and processes
Executing rules
Integrating rules with existing applications and Rule Maintenance
Rule integration
PHP Unit tests and automated testing
DDD and TDD with Business rules

This four day course is aimed at teaching how genetic algorithms work; it also covers how to select model parameters of a genetic algorithm; there are many applications for genetic algorithms in this course and optimization problems are tackled with the genetic algorithms.
Day 1:
What is a genetic algorithm?
Chromosome fitness
Choosing the random initial population
The crossover operations
A numeric optimzation example
Day 2
When to use genetic algorithm
Coding the gene
Local maximums and mutation operation
Population diversity
Day 3
The meaning and effect of each genetic algorithm parameter
Varying genetic parameters
Optimizing scheduling problems
Cross over and mutation for scheduling problems
Day 4
Optimizing program or set of rules
Cross over and mutation operations for optimizing programs
Creating a parallel model of the genetic algorithm
Evaluating the genetic algorithm
Applications of genetic algorithm

This training course is for people that would like to apply basic Machine Learning techniques in practical applications.
Audience
Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work
Sector specific examples are used to make the training relevant to the audience.
Naive Bayes
Multinomial models
Bayesian categorical data analysis
Discriminant analysis
Linear regression
Logistic regression
GLM
EM Algorithm
Mixed Models
Additive Models
Classification
KNN
Ridge regression
Clustering

This course is aimed at enterprise architects, business and system analysts and managers who want to apply business rules to their solution. With Drools you can write your business rules using almost natural language, therefore reducing the gap between business and IT.
Short Introduction to Rule Engines
Artificial Intelligence
Expert Systems
What is a Rule Engine?
Why use a Rule Engine?
Advantages of a Rule Engine
When should you use a Rule Engine?
Scripting or Process Engines
When you should NOT use a Rule Engine
Strong and Loose Coupling
What are rules?
Creating and Implementing Rules
Fact Model
KIE
Spreadsheet
Eclipse
Domain Specific Language (DSL)
Replacing rules with DSL
Testing DSL rules
jBPM
Integration with Drools
Fusion
What is Complex Event Processing?
Short overview on Fusion
Rules Testing
Testing with KIE
Testing with JUnit
Integrating Rules with Applications
Invoking rules from Java Code

This four day course walks you from the point you design your research surveys to the tme where you gather and collect the findings of the survey. The course is based on Excel and Matlab. You will learn how to design the survey form and what the suitable data fields should be, and how to process extra data information when needed. The course will show you the way the data is entered and how to validate and correct wrong data values. At the end the data analysis will be conducted in a variety of ways to ensure the effectiveness of the data gathered and to find out hidden trends and knowledge within this data. A number of case studies will be carried out during the course to make sure all the concepts have been well understood.
Day 1:
Data analysis
Determining the Target of the survey
Survey Design
data fields and their types
dealing with drill down surveies
Data Collection
Data Entry
Excel Session
Day 2:
Data cleaning
Data reduction
Data Sampling
Removing unexpcted data
Removing outlier
Data Analysis
statstics is not enough
Excel Session
Day 3:
Data visualization
parallel cooridnates
scatter plot
pivot tables
cross tables
Excel Session
Conducting data mining algorithms on the data
Decision tree
Clustering
mining assoiciation rules
matlab session
Day 4:
Reporting and Disseminating Results
Archiving data and the finding out
Feedback for conducting new surveies

This course is intended for engineers and decision makers working in data mining and knoweldge discovery.
You will learn how to create effective plots and ways to present and represent your data in a way that will appeal to the decision makers and help them to understand hidden information.
Day 1:
what is data visualization
why it is important
data visualization vs data mining
human cognition
HMI
common pitfalls
Day 2:
different type of curves
drill down curves
categorical data plotting
multi variable plots
data glyph and icon representation
Day 3:
plotting KPIs with data
R and X charts examples
what if dashboards
parallel axes mixing
categorical data with numeric data
Day 4:
different hats of data visualization
how can data visualization lie
disguised and hidden trends
a case study of student data
visual queries and region selection

Audience:
The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment
Goal:
Deep knowledge on Hadoop cluster administration.
1: HDFS (17%)
Describe the function of HDFS Daemons
Describe the normal operation of an Apache Hadoop cluster, both in data storage and in data processing.
Identify current features of computing systems that motivate a system like Apache Hadoop.
Classify major goals of HDFS Design
Given a scenario, identify appropriate use case for HDFS Federation
Identify components and daemon of an HDFS HA-Quorum cluster
Analyze the role of HDFS security (Kerberos)
Determine the best data serialization choice for a given scenario
Describe file read and write paths
Identify the commands to manipulate files in the Hadoop File System Shell
2: YARN and MapReduce version 2 (MRv2) (17%)
Understand how upgrading a cluster from Hadoop 1 to Hadoop 2 affects cluster settings
Understand how to deploy MapReduce v2 (MRv2 / YARN), including all YARN daemons
Understand basic design strategy for MapReduce v2 (MRv2)
Determine how YARN handles resource allocations
Identify the workflow of MapReduce job running on YARN
Determine which files you must change and how in order to migrate a cluster from MapReduce version 1 (MRv1) to MapReduce version 2 (MRv2) running on YARN.
3: Hadoop Cluster Planning (16%)
Principal points to consider in choosing the hardware and operating systems to host an Apache Hadoop cluster.
Analyze the choices in selecting an OS
Understand kernel tuning and disk swapping
Given a scenario and workload pattern, identify a hardware configuration appropriate to the scenario
Given a scenario, determine the ecosystem components your cluster needs to run in order to fulfill the SLA
Cluster sizing: given a scenario and frequency of execution, identify the specifics for the workload, including CPU, memory, storage, disk I/O
Disk Sizing and Configuration, including JBOD versus RAID, SANs, virtualization, and disk sizing requirements in a cluster
Network Topologies: understand network usage in Hadoop (for both HDFS and MapReduce) and propose or identify key network design components for a given scenario
4: Hadoop Cluster Installation and Administration (25%)
Given a scenario, identify how the cluster will handle disk and machine failures
Analyze a logging configuration and logging configuration file format
Understand the basics of Hadoop metrics and cluster health monitoring
Identify the function and purpose of available tools for cluster monitoring
Be able to install all the ecosystem components in CDH 5, including (but not limited to): Impala, Flume, Oozie, Hue, Manager, Sqoop, Hive, and Pig
Identify the function and purpose of available tools for managing the Apache Hadoop file system
5: Resource Management (10%)
Understand the overall design goals of each of Hadoop schedulers
Given a scenario, determine how the FIFO Scheduler allocates cluster resources
Given a scenario, determine how the Fair Scheduler allocates cluster resources under YARN
Given a scenario, determine how the Capacity Scheduler allocates cluster resources
6: Monitoring and Logging (15%)
Understand the functions and features of Hadoop’s metric collection abilities
Analyze the NameNode and JobTracker Web UIs
Understand how to monitor cluster Daemons
Identify and monitor CPU usage on master nodes
Describe how to monitor swap and memory allocation on all nodes
Identify how to view and manage Hadoop’s log files
Interpret a log file

This training course is for people that would like to apply Machine Learning in practical applications.
Audience
This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.
The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.
Sector specific examples are used to make the training relevant to the audience.
Naive Bayes
Multinomial models
Bayesian categorical data analysis
Discriminant analysis
Linear regression
Logistic regression
GLM
EM Algorithm
Mixed Models
Additive Models
Classification
KNN
Bayesian Graphical Models
Factor Analysis (FA)
Principal Component Analysis (PCA)
Independent Component Analysis (ICA)
Support Vector Machines (SVM) for regression and classification
Boosting
Ensemble models
Neural networks
Hidden Markov Models (HMM)
Space State Models
Clustering

Audience
If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.
It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.
It is not aimed at people configuring the solution, those people will benefit from the big picture though.
Delivery Mode
During the course delegates will be presented with working examples of mostly open source technologies.
Short lectures will be followed by presentation and simple exercises by the participants
Content and Software used
All software used is updated each time the course is run so we check the newest versions possible.
It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.
Quick Overview
Data Sources
Minding Data
Recommender systems
Target Marketing
Datatypes
Structured vs unstructured
Static vs streamed
Attitudinal, behavioural and demographic data
Data-driven vs user-driven analytics
data validity
Volume, velocity and variety of data
Models
Building models
Statistical Models
Machine learning
Data Classification
Clustering
kGroups, k-means, nearest neighbours
Ant colonies, birds flocking
Predictive Models
Decision trees
Support vector machine
Naive Bayes classification
Neural networks
Markov Model
Regression
Ensemble methods
ROI
Benefit/Cost ratio
Cost of software
Cost of development
Potential benefits
Building Models
Data Preparation (MapReduce)
Data cleansing
Choosing methods
Developing model
Testing Model
Model evaluation
Model deployment and integration
Overview of Open Source and commercial software
Selection of R-project package
Python libraries
Hadoop and Mahout
Selected Apache projects related to Big Data and Analytics
Selected commercial solution
Integration with existing software and data sources

Estimates for Internet of Things or IoT market value are massive, since by definition the IoT is an integrated and diffused layer of devices, sensors, and computing power that overlays entire consumer, business-to-business, and government industries. The IoT will account for an increasingly huge number of connections: 1.9 billion devices today, and 9 billion by 2018. That year, it will be roughly equal to the number of smartphones, smart TVs, tablets, wearable computers, and PCs combined.
In the consumer space, many products and services have already crossed over into the IoT, including kitchen and home appliances, parking, RFID, lighting and heating products, and a number of applications in Industrial Internet.
However the underlying technologies of IoT are nothing new as M2M communication existed since the birth of Internet. However what changed in last couple of years is the emergence of number of inexpensive wireless technologies added by overwhelming adaptation of smart phones and Tablet in every home. Explosive growth of mobile devices led to present demand of IoT.
Due to unbounded opportunities in IoT business, a large number of small and medium sized entrepreneurs jumped on a bandwagon of IoT gold rush. Also due to emergence of open source electronics and IoT platform, cost of development of IoT system and further managing its sizable production is increasingly affordable. Existing electronic product owners are experiencing pressure to integrate their device with Internet or Mobile app.
This training is intended for a technology and business review of an emerging industry so that IoT enthusiasts/entrepreneurs can grasp the basics of IoT technology and business.
Course objectives
Main objective of the course is to introduce emerging technological options, platforms and case studies of IoT implementation in home & city automation (smart homes and cities), Industrial Internet, healthcare, Govt., Mobile Cellular and other areas.
Basic introduction of all the elements of IoT-Mechanical, Electronics/sensor platform, Wireless and wireline protocols, Mobile to Electronics integration, Mobile to enterprise integration, Data-analytics and Total control plane
M2M Wireless protocols for IoT- WiFi, Zigbee/Zwave, Bluetooth, ANT+ : When and where to use which one?
Mobile/Desktop/Web app- for registration, data acquisition and control –Available M2M data acquisition platform for IoT-–Xively, Omega and NovoTech, etc.
Security issues and security solutions for IoT
Open source/commercial electronics platform for IoT-Raspberry Pi, Arduino , ArmMbedLPC etc
Open source /commercial enterprise cloud platform for IoT-Ayla, iO Bridge, Libellium, Axeda, Cisco fog cloud
Studies of business and technology of some of the common IoT devices like Home automation, Smoke alarm, vehicles, military, home health etc
Target Audience
Investors and IoT entrepreneurs
Managers and Engineers whose company is venturing into IoT space
Business Analysts & Investors
Pre-requisites
Should have basic knowledge of business operation, devices, electronics systems and data systems
Must have basic understanding of software and systems
Basic understanding of Statistics ( in Excel levels)
1. Day 1, Session 1 — Business Overview of Why IoT is so important
Case Studies from Nest, CISCO and top industries
IoT adaptation rate in North American & and how they are aligning their future business model and operation around IoT
Broad Scale Application Area
Smart House and Smart City
Industrial Internet
Smart Cars
Wearables
Home Healthcare
Business Rule Generation for IoT
3 layered architecture of Big Data — Physical (Sensors), Communication, and Data Intelligence
2. Day 1, Session 2 — Introduction of IoT: All about Sensors – Electronics
Basic function and architecture of a sensor — sensor body, sensor mechanism, sensor calibration, sensor maintenance, cost and pricing structure, legacy and modern sensor network — all the basics about the sensors
Development of sensor electronics — IoT vs legacy, and open source vs traditional PCB design style
Development of sensor communication protocols — history to modern days. Legacy protocols like Modbus, relay, HART to modern day Zigbee, Zwave, X10,Bluetooth, ANT, etc.
Business driver for sensor deployment — FDA/EPA regulation, fraud/tempering detection, supervision, quality control and process management
Different Kind of Calibration Techniques — manual, automation, infield, primary and secondary calibration — and their implication in IoT
Powering options for sensors — battery, solar, Witricity, Mobile and PoE
Hands on training with single silicon and other sensors like temperature, pressure, vibration, magnetic field, power factor etc.
3. Day 1, Session 3 — Fundamental of M2M communication — Sensor Network and Wireless protocol
What is a sensor network? What is ad-hoc network?
Wireless vs. Wireline network
WiFi- 802.11 families: N to S — application of standards and common vendors.
Zigbee and Zwave — advantage of low power mesh networking. Long distance Zigbee. Introduction to different Zigbee chips.
Bluetooth/BLE: Low power vs high power, speed of detection, class of BLE. Introduction of Bluetooth vendors & their review.
Creating network with Wireless protocols such as Piconet by BLE
Protocol stacks and packet structure for BLE and Zigbee
Other long distance RF communication link
LOS vs NLOS links
Capacity and throughput calculation
Application issues in wireless protocols — power consumption, reliability, PER, QoS, LOS
Hands on training with sensor network
PICO NET- BLE Base network
Zigbee network-master/slave communication
Data Hubs : MC and single computer ( like Beaglebone ) based datahub
4. Day 1, Session 4 — Review of Electronics Platform, production and cost projection
PCB vs FPGA vs ASIC design-how to take decision
Prototyping electronics vs Production electronics
QA certificate for IoT- CE/CSA/UL/IEC/RoHS/IP65: What are those and when needed?
Basic introduction of multi-layer PCB design and its workflow
Electronics reliability-basic concept of FIT and early mortality rate
Environmental and reliability testing-basic concepts
Basic Open source platforms: Arduino, Raspberry Pi, Beaglebone, when needed?
RedBack, Diamond Back
5. Day 2, Session 1 — Conceiving a new IoT product- Product requirement document for IoT
State of the present art and review of existing technology in the market place
Suggestion for new features and technologies based on market analysis and patent issues
Detailed technical specs for new products- System, software, hardware, mechanical, installation etc.
Packaging and documentation requirements
Servicing and customer support requirements
High level design (HLD) for understanding of product concept
Release plan for phase wise introduction of the new features
Skill set for the development team and proposed project plan -cost & duration
Target manufacturing price
6. Day 2, Session 2 — Introduction to Mobile app platform for IoT
Protocol stack of Mobile app for IoT
Mobile to server integration –what are the factors to look out
What are the intelligent layer that can be introduced at Mobile app level ?
iBeacon in IoS
Window Azure
Linkafy Mobile platform for IoT
Axeda
Xively
7. Day 2, Session 3 — Machine learning for intelligent IoT
Introduction to Machine learning
Learning classification techniques
Bayesian Prediction-preparing training file
Support Vector Machine
Image and video analytic for IoT
Fraud and alert analytic through IoT
Bio –metric ID integration with IoT
Real Time Analytic/Stream Analytic
Scalability issues of IoT and machine learning
What are the architectural implementation of Machine learning for IoT
8. Day 2, Session 4 — Analytic Engine for IoT
Insight analytic
Visualization analytic
Structured predictive analytic
Unstructured predictive analytic
Recommendation Engine
Pattern detection
Rule/Scenario discovery — failure, fraud, optimization
Root cause discovery
9. Day 3, Session 1 — Security in IoT implementation
Why security is absolutely essential for IoT
Mechanism of security breach in IOT layer
Privacy enhancing technologies
Fundamental of network security
Encryption and cryptography implementation for IoT data
Security standard for available platform
European legislation for security in IoT platform
Secure booting
Device authentication
Firewalling and IPS
Updates and patches
10. Day 3, Session 2 — Database implementation for IoT : Cloud based IoT platforms
SQL vs NoSQL-Which one is good for your IoT application
Open sourced vs. Licensed Database
Available M2M cloud platform
Axeda
Xively
Omega
NovoTech
Ayla
Libellium
CISCO M2M platform
AT &T M2M platform
Google M2M platform
11. Day 3, Session 3 — A few common IoT systems
Home automation
Energy optimization in Home
Automotive-OBD
IoT-Lock
Smart Smoke alarm
BAC ( Blood alcohol monitoring ) for drug abusers under probation
Pet cam for Pet lovers
Wearable IOT
Mobile parking ticketing system
Indoor location tracking in Retail store
Home health care
Smart Sports Watch
12. Day 3, Session 4 — Big Data for IoT
4V- Volume, velocity, variety and veracity of Big Data
Why Big Data is important in IoT
Big Data vs legacy data in IoT
Hadoop for IoT-when and why?
Storage technique for image, Geospatial and video data
Distributed database
Parallel computing basics for IoT

Caffe is a deep learning framework made with expression, speed, and modularity in mind.
This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example
Audience
This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.
After completing this course, delegates will be able to:
understand Caffe’s structure and deployment mechanisms
carry out installation / production environment / architecture tasks and configuration
assess code quality, perform debugging, monitoring
implement advanced production like training models, implementing layers and logging
Installation
Docker
Ubuntu
RHEL / CentOS / Fedora installation
Windows
Caffe Overview
Nets, Layers, and Blobs: the anatomy of a Caffe model.
Forward / Backward: the essential computations of layered compositional models.
Loss: the task to be learned is defined by the loss.
Solver: the solver coordinates model optimization.
Layer Catalogue: the layer is the fundamental unit of modeling and computation – Caffe’s catalogue includes layers for state-of-the-art models.
Interfaces: command line, Python, and MATLAB Caffe.
Data: how to caffeinate data for model input.
Caffeinated Convolution: how Caffe computes convolutions.
New models and new code
Detection with Fast R-CNN
Sequences with LSTMs and Vision + Language with LRCN
Pixelwise prediction with FCNs
Framework design and future
Examples:
MNIST

This course has been prepared for people who are involved in administering corporate knowledge assets (rules, process) like system administrators, system integrators, application server administrators, etc...
We are using the newest stable community version of Drools to run this course, but older versions are also possible if agreed before booking.Drools Administration
Short Introduction to Rule Engines
Artificial Intelligence
Expert Systems
What is a Rule Engine?
Why use a Rule Engine?
Advantages of a Rule Engine
When should you use a Rule Engine?
Scripting or Process Engines
When you should NOT use a Rule Engine
Strong and Loose Coupling
What are rules?
Where things are
Managing rules in a jar file
Git repository
Executing rules from KIE
Managing BPMN and workflows files
Moving knowledge files (rules, processes, forms, work times...)
Rules Testing
Where to store test
How to execute tests
Testing with JUnit
Deployment Strategies
stand alone application
Invoking rules from Java Code
integration via files (json, xml, etc...)
integration via web services
using KIE for integration
Administration of rules
authoring
Packages
Artifact Repository
Asset Editor
Validation
Data Model
Categories
versioning
Domain Specific Languages
Optimizing hardware and software for rules execution
Multithreading and Drools
Kie
Projects structures
Lifecycles
Building
Deploying
Running
Installation and Deployment Cheat Sheets
Organization Units
Users, Rules and Permissions
Authentication
Repositories
Backup and Restore
Logging

The course is dedicated to IT engineers and architects who are looking for a solution to host private or public IaaS (Infrastructure as a Service) cloud.
This is also great opportunity for IT managers to gain knowledge owerview about possibilities which could be enabled by OpenStack.
Before You spend a lot of money on OpenStack implementation, You could consider all pros and cons by attending on our course.
This topic is also avaliable as individual consultancy.
Course goal:
gaining basic knowledge regarding OpenStack
Introduction:
What is OpenStack?
Foundations of Cloud Computing
OpenStack vs VMware
OpenStack evolution
OpenStack distributions
OpenStack releases
OpenStack deployment solutions
OpenStack competitors
OpenStack Services:
Underpinning services
Keystone
Glance
Nova
Neutron
Cinder
Horizon
Swift
Heat
Ceilometer
Trove
Sahara
Ironic
Zaqar
Manila
Designate
Barbican
OpenStack Architecture:
Node roles
High availability
Scalability
Segregation
Backup
Monitoring
Self service portal
Interfaces
Quotas
Workflows
Schedulers
Migrations
Load balancing
Autoscaling
Demonstration:
How to download and execute RC files
How to create an external network in Neutron
How to upload an image to Glance
How to create a new flavor in Nova
How to update default Nova and Neutron quotas
How to create a new tenant in Keystone
How to create a new user in Keystone
How to manage roles in Keystone
How to create a tenant network in Neutron
How to create a router in Neutron
How to manage router’s interfaces in Neutron
How to update security groups in Neutron
How to upload RSA key-pair to the project
How to allocate floating IPs to the project
How to launch an instance from image in Nova
How to associate floating IPs with instances
How to create a new volume in Cinder
How to attach the volume to the instance
How to take a snapshot of the instance
How to take a snapshot of the volume
How to launch an instance from snapshot in Nova
How to create a volume from snapshot in Cinder

The course is intended for IT specialist that works with the distributed processing of large data sets across clusters of computers.
Data Mining and Business Intelligence
Introduction
Area of application
Capabilities
Basics of data exploration
Big data
What does Big data stand for?
Big data and Data mining
MapReduce
Model basics
Example application
Stats
Cluster model
Hadoop
What is Hadoop
Installation
Configuration
Cluster settings
Architecture and configuration of Hadoop Distributed File System
Console tools
DistCp tool
MapReduce and Hadoop
Streaming
Administration and configuration of Hadoop On Demand
Alternatives

This course has been designed for people interested in extracting meaning from written English text, though the knowledge can be applied to other human languages as well.
The course will cover how to make use of text written by humans, such as blog posts, tweets, etc...
For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.
Short Introduction to NLP methods
word and sentence tokenization
text classification
sentiment analysis
spelling correction
information extraction
parsing
meaning extraction
question answering
Overview of NLP theory
probability
statistics
machine learning
n-gram language modeling
naive bayes
maxent classifiers
sequence models (Hidden Markov Models)
probabilistic dependency
constituent parsing
vector-space models of meaning

This course is aimed at enterprise architects, business and system analysts, technical managers and developers who want to apply business rules to their solutions.
This course contains a lot of simple hands-on exercises during which the participants will create working rules. Please refer to our other courses if you just need an overview of Drools.
This course is usually delivered on the newest stable version of Drools and jBPM, but in case of a bespoke course, can be tailored to a specific version.
Short Introduction to Rule Engines
Artificial Intelligence
Expert Systems
What is a Rule Engine?
Why use a Rule Engine?
Advantages of a Rule Engine
When should you use a Rule Engine?
Scripting or Process Engines
When you should NOT use a Rule Engine
Strong and Loose Coupling
What are rules?
Creating and Implementing Rules
Fact Model
KIE
Rules visioning and repository
Exercises
Domain Specific Language (DSL)
Replacing rules with DSL
Testing DSL rules
Exercises
jBPM
Integration with Drools
Short overview of basic BPMN
Invoking rules from a processes
Grouping rules
Exercises
Fusion
What is Complex Event Processing?
Short overview on Fusion
Exercises
Mvel - the rule language
Filtering (fact type, field
Operators
Compound conditions
Operators priority
Accumulate Functions (average, min, max, sum, collectList, etc....)
Rete - under the hood
Compilation algorithm
Drools RETE extensions
Node Types
Understating Rete Tree
Rete Optimization
Rules Testing
Testing with KIE
Testing with JUnit
OptaPlanner
An overview of OptaPlanner
Simple examples
Integrating Rules with Applications
Invoking rules from Java Code

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